JANUARY 2019

Do you know how to leverage next-gen technologies like automation and AI to deliver core value to your business? Check out our new thought leadership: Validation of Machine Learning Models.

Validation of Machine Learning Models

Challenges and Alternatives

The potential of machine learning (ML) to deliver value to banks has created something of a gold rush in adopting this methodology for banking applications. ML can produce immense benefits when applied to complex nonlinear problems where there is a large amount of data, particularly unstructured data. Use cases for incorporating machine learning in banking include asset management, fraud detection, credit risk management and regulatory compliance, to name a few. More specifically, large banks are turning to ML models as an alternative to traditional models to gain faster, more accurate and more insightful predictions and classifications in their risk management and financial management business decisions.